Youth Injury Knowledge and Beliefs following Neuromuscular Training Warm-up Implementation in Schools
Bibliographic record
Abstract
Neuromuscular training warm-up programs can reduce injury rates in youth sports, but they often have poor uptake and adherence. Delivering such programs in school physical education classes may provide greater public health benefit, particularly if they promote improved injury knowledge and prevention beliefs amongst students. The purpose of this secondary analysis of a large cluster-randomized controlled trial was to understand how students' (age 11-15 years) knowledge and beliefs change after exposure to an evidence-informed neuromuscular training warm-up program. Six schools delivered the program for a 12-week period in the initial study year (n=566) and two continued to use it in a subsequent "maintenance" year (n=255). Students completed a knowledge and beliefs questionnaire at baseline, 6-week, and 12-week timepoints. Knowledge scores ranged from 7/10 to 8/10 at all timepoints and students generally believed that injuries are preventable. On average, there was less than a one-point change in knowledge between timepoints and there was no change in the median belief scores. There were no meaningful differences between sexes, grades, or previous injury. These findings highlight that knowledge and beliefs are unlikely to change passively through program exposure. More active strategies are needed to improve injury prevention perceptions in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".